CD4+ T-Cell Count Monitoring Does Not Accurately Identify HIV-Infected Adults With Virologic Failure Receiving Antiretroviral Therapy
Bibliographic record
Abstract
BACKGROUND: CD4 T-lymphocyte (CD4) counts are widely used to monitor response to antiretroviral therapy (ART) in resource-limited settings. However, the utility of such monitoring in terms of predicting virologic response to therapy has been little studied. METHODS: We studied participants aged 18 years and older who initiated ART in Tororo, Uganda. CD4 counts, CD4 percentages, and viral load (VL) were examined at 6-monthly intervals. Various definitions of immunologic failure were examined to identify individuals with VLs>or=50, >or=500, >or=1000, or >or=5000 copies per milliliter at 6, 12, and 18 months after treatment initiation. RESULTS: One thousand sixty-three ART-naive persons initiated ART. The proportion of individuals with virologic failure ranged between 1.5% and 16.4% for each time point. The proportion with no increase in CD4 count from baseline did not differ between those with suppressed or unsuppressed VLs at 6, 18, and 24 months after ART initiation. No increase in CD4 cell counts at 6 months had a sensitivity of 0.04 [95% confidence interval (CI) 0.00 to 0.10] and a positive predictive value of 0.03 (95% CI 0.00 to 0.09) for identifying individuals with VL>or=500 copies per milliliter at 6 months. The best measure identified was an absolute CD4 cell count<125 cells per microliter at 21 months for predicting VL>or=500 copies per milliliter at 18 months which had a sensitivity of 0.13 (95% CI 0.01 to 0.21) and a positive predictive value of 0.29 (95% CI 0.10 to 0.44). CONCLUSIONS: CD4 cell count monitoring does not accurately identify individuals with virologic failure among patients taking ART.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".